A crucial task in polymer chemistry is the formulation of materials which satisfy strict property constraints. This paper describes the use of dynamic adaptive agents in the design of novel polymers. A neural network is used to predict the properties of a proposed polymer from its composition, while a genetic algorithm solves the inverse problem by acting as a search agent to find promising formulations. The resulting Hybrid Intelligent System provides a computationally efficient means of searching the virtual space defined by the set of all feasible polymers. We report here results from two initial studies.